The present invention relates generally to computer imaging, and more particularly to improved image blur detection methods and arrangements based on edge detection and follow-on comparison calculations.
With the increasing popularity of personal computers, handheld appliances and the like, there has been a corresponding increase in the popularity and affordability of image rendering/manipulation applications.
Thus, for example, many personal computers and workstations are being configured as multimedia devices that are capable of receiving image data, for example, directly from a digital camera or indirectly from another networked device. These so-called multimedia devices are further configured to display the image data (e.g., still images, video, etc.). As for still images and single video frames, most multimedia devices can be further coupled to a printing device that is configured to provide a printed hardcopy of the image data.
When provided with the appropriate software application(s), the multimedia device can be configured to allow the user to manipulate all or portions of the image data in some manner. For example, there is a variety of photo/drawing manipulation applications and video editing applications available today. One example of a photo/drawing manipulation program is PhotoDraw(copyright) 2000, available from the Microsoft Corporation of Redmond, Wash. Another example of an image manipulation program is Picture It! 2000, also available from the Microsoft Corporation. One example of a video editing application is Adobe Premiere 6.0 available from Adobe Systems Incorporated of San Jose, Calif.
These and other image manipulation programs provide a multitude of image editing tools/features. In some instances, for example, in the key-frame evaluation and photo quality estimation features of Picture It! 2000, the image manipulation program may need to calculate certain characteristics associated with the image data in terms of its"" blurriness/sharpness. Doing so allows for the user and/or the application to selectively or automatically manipulate blurred image data in some desired fashion. For example, a blurred portion of the image may be sharpened or perhaps protected from additional blurring.
With this in mind, previous methods for calculating blur characteristics have been designed for image restoration. By way of example, see the article by M. C. Chiang and T. E. Boult, titled xe2x80x9cLocal Blur Estimation and Super-Resolutionxe2x80x9d, as published in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 821-826, June 1997. Also, for example, see the article by R. L. Lagendijk, A. M. Tekalp and J. Biemond, titled xe2x80x9cMaximum Likelihood Image and Blur Identification: A Unifying Approachxe2x80x9d as published in Optical Engineering, 29(5):422-435, May 1990.
These exemplary conventional techniques utilize methods that estimate the parameters needed by the reverse process of blur. Unfortunately, these methods tend to be complex and time-consuming.
Still other techniques utilize compressed domain methods based on discrete cosign transform (DCT) coefficient statistics, which can be used to estimate the blurriness of motion picture expert group (MPEG) frame in real-time. For example, see the methods presented by Xavier Marichal, Wei-Ying Ma and HongJiang Zhang at the International Conference on Image Processing (ICIP) in Kobe, Japan on Oct. 25-29, 1999, as published in an article titled xe2x80x9cBlur Determination in the Compressed Domain Using DCT Informationxe2x80x9d. Unfortunately, these methods often find it difficult to handle images with relatively large uni-color patches.
Hence, there is an on-going need for new and improved methods for calculating or otherwise determining blurriness/sharpness characteristics in an image.
The present invention provides new and improved methods and arrangements for calculating blurriness/sharpness characteristics in an image. In accordance with certain aspects of the present invention, the methods and arrangements can be provided in a variety of devices or appliances and used to support image rendering/presentation processes, image manipulation processes, and/or other like image data related processes. In accordance with certain exemplary implementations of the present invention, the improved methods and arrangements employ a multi-scale edge amplitude comparison to evaluate the quality of an image, rather than estimating blurriness characteristics as in the conventional methods described above. Furthermore, in certain implementations, the multi-scale edge amplitude comparison is automatically adaptable to the image content.
Thus, for example, in accordance with certain exemplary implementations of the present invention, the above stated needs and others are met by a method that includes detecting edges in a plurality of corresponding different resolution images, and for each detected edge, comparing corresponding edge parameters associated with the detected edges in the plurality of corresponding different resolution images and determining if the detected edge is blurred. In certain implementations the edge parameters associated with the detected edges include edge amplitudes.
The method may also include generating the plurality of corresponding different resolution images from a base image, such that the resulting plurality of corresponding different resolution images includes the base image and at least one corresponding lower resolution image. In certain implementations, for example, the plurality of corresponding different resolution images includes the base image a second corresponding lower resolution image, and a third corresponding lower resolution image that is also lower in resolution than the second corresponding lower resolution image.
In detecting the edges in the plurality of corresponding different resolution images, the method may further include generating a corresponding plurality of detected edge maps. In such a case, the step of comparing the corresponding edge parameters associated with the detected edges may also include comparing corresponding edge amplitudes as provided in the plurality of detected edge maps to generate a result map.
The method may include the additional step of calculating a blur parameter based on this result map. For example, the blur parameter might include a blur percentage.
In still other implementations, the method may also include the step of determining if the base image is blurred based on a comparison of the blur parameter with at least one blur parameter threshold.
In accordance with certain further implementations of the present invention, an apparatus is provided. The apparatus includes an edge detector that is configured to detect edge transitions in a plurality of corresponding different resolution images, an edge parameter comparator that is configured to compare corresponding edge parameters as detected by the edge detector, and a blur calculator that is configured to determine at least one blur parameter based on comparison results as determined by the edge parameter comparator. The apparatus may also include an image generator that is configured to generate the plurality of corresponding different resolution images based on a base image, and provide the plurality of corresponding different resolution images to the edge detector. In still further implementations, the apparatus may include a blur detector that is configured to determine if a base image is blurred based on a comparison of the at least one blur parameter with at least one blur parameter threshold.